Introduction to the special section: the importance of behavioral medicine in the COVID-19 pandemic response
Bibliographic record
Abstract
Preventative behaviors, including getting vaccinated, wearing a mask, and physical distancing, are at the heart of controlling the spread of COVID-19. Currently, vaccination has become the cornerstone of most governments’ strategies to minimize viral transmission and reduce the number of hospitalizations and deaths. However, vaccine hesitancy is still a major problem across most countries, with unvaccinated people at the highest risk of becoming infected and hospitalized. As we have seen most recently with the omicron variant, the hospitalization and care of unvaccinated people increases the potential for health care systems to become overwhelmed [1]. As behavior is at the heart of managing health during the pandemic, governments have used a number of different mechanisms to motivate individuals to engage in preventative behaviors, ranging from threatening messages to minimizing barriers to incentives such as lottery tickets [2,3]. These measures have had an inconsistent impact on...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.007 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".